Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO
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- 作者：Fani Boukouvalaa ; b ; Ruth Misenerc ; Christodoulos A. Floudas ; a ; b ; floudas@princeton.edu" class="auth_mail" title="E-mail the corresponding author ; floudas@titan.princeton.edu" class="auth_mail" title="E-mail the corresponding author ; floudas@tamu.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：MINLP ; Deterministic global optimization ; Derivative-free ; Grey-/Black-box ; Constraints
- 刊名：European Journal of Operational Research
- 出版年：2016
- 出版时间：1 August 2016
- 年：2016
- 卷：252
- 期：3
- 页码：701-727
- 全文大小：2653 K
文摘
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We review the recent advances in global optimization for Mixed Integer Nonlinear Programming, MINLP.
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We review the recent advances in global optimization for Constrained Derivative-Free optimization, CDFO.
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We present theoretical contributions, software implementations and applications for both MINLP and CDFO.
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We discuss possible interactions between the two areas of MINLP and CDFO.
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We present a complete test suite for MINLP and CDFO algorithms.